Entries in jclouds
(2)

The term "cloud portability" is often considered a synonym for "Cloud API portability," which implies a series of misconceptions.

If we break away from dogma, we can find that what we really looking for in cloud portability is Application portability between clouds which can be a vastly simpler requirement, as we can achieve application portability without settling on a common Cloud API.

In this post i'll be covering five common misconceptions people have WRT to cloud portability.

The main incentive for Cloud Portability is - Avoiding Vendor lock-in.Cloud portability is more about business agility than it is about vendor lock-in.

Cloud portability isn’t for startups. Every startup that is expecting rapid growth should re-examine their deployments and plan for cloud portability rather than wait to be forced to make the switch when you are least prepared to do so.

Cloud portability = Compromising on the least common denominator.Application portability doesn't require compromise on the least common denominator as most of the interaction with the cloud API happens outside of our application code anyway, to handle things like provisioning, setup, installation, scaling, monitoring, etc.

The effort for achieving cloud portability far exceed the value. The effort to achieve cloud portability is far less than it used to be, in most cases, making it a greater and more valuable priority (with less investment) than it used to be.

A demonstration, with repeatable steps, of how to quickly fire-up a Hadoop cluster on Amazon EC2, load data onto the HDFS (Hadoop Distributed File-System), write map-reduce scripts in Ruby and use them to run a map-reduce job on your Hadoop cluster. You will not need to ssh into the cluster, as all tasks are run from your local machine. Below I am using my MacBook Pro as my local machine, but the steps I have provided should be reproducible on other platforms running bash and Java.